Discriminatory ability of perioperative heart rate variability in predicting postoperative complications in major urologic surgery: a prospective cohort study

We aimed to determine if continuous perioperative heart rate variability (HRV) monitoring could improve risk stratification compared to a short preoperative measurement in radical cystectomy patients. Electrocardiography (ECG) recordings were collected continuously preoperatively to discharge in 83 patients. Two, 5-min ECG signal segments (preoperative and at 24-h post ECG placement) were analyzed offline to extract HRV metrics. HRV metric discriminatory ability to identify patients with 30-day postoperative complications were analyzed using receiver operating characteristics curves. Sixty participants were included for analysis of which 27 (45%) developed a complication within 30 days postoperative. HRV was reduced in patients with complications. Postoperative standard deviation NN intervals and root mean square of successive differences had area under the curves (AUC) of 0.67 (95% CI 0.54 to 0.81) and 0.68 (95% CI 0.54 to 0.82), respectively. Significant discriminatory abilities were also reported for postoperative frequency metrics of absolute low frequency (LF) [AUC = 0.65 (95% CI 0.51 to 0.79)] and high frequency (HF) powers [AUC = 0.69 (95% CI 0.55 to 0.83)] and total power [AUC = 0.66 (95% CI 0.53 to 0.80)]. Postoperative acquired HRV metrics demonstrated improved discriminatory ability. Our findings suggest that longer-term perioperative HRV monitoring presents with superior ability to stratify complication risk.


Procedure
All participants had a modified Biotricity Bioflux 1 (Biotricity, Redwood City, California, USA), a continuous, ambulatory cloud-based and Food and Drug Administration (FDA) approved ECG monitor applied preoperatively.Three wet contact lead ECG patches were applied at the left and right infraclavicular fossa and the lower chest to the left of the umbilicus.The monitor remained in place for the duration of hospital stay with intermittent charging performed daily.The device was connected and data was continuously collected at 1000 Hz from the time of ECG patch placement until discharge or participant request to remove.Consented participants were placed on the Enhanced Recovery After Surgery (ERAS) protocol for preoperative, intraoperative, and postoperative management 23 .Patients received balanced anesthesia including standard induction agents of propofol, opioid, neuromuscular blocker, maintenance with volatile anesthesia or hypnotic infusion, multimodal analgesia, and antiemetics.Patients were admitted to the urology unit for postoperative care.
ECG signals were analyzed offline to extract HRV metrics (see Supplemental Information S1) in both time [standard deviation of normal NN intervals (SDNN), root mean squared standard deviation (RMSSD)] and frequency [absolute very low frequency (VLF) power, absolute low frequency (LF) power, LF power in normalized units, absolute high frequency (HF) power, HF power in normalized units and total power] domains using Kubios HRV Premium software version 3.4.1 (Kubios Oy, Kuopio, Finland) and in accordance with The Task Force of European Society of Cardiology and the North American Society of Pacing and Electrophysiology standards with respect to HRV analysis 24 .
We targeted two time points to acquire a 5-min ECG segment.We attempted to standardize environmental conditions during acquisition.Our first 5-min segment was targeted preoperatively between 10 min prior to up to the time of anesthesia induction.At this time, all participants were prepped for surgery and lying supine in the operating suite.Our second 5-min segment target was 24-h ± 1 h after ECG device placement.Within each target window, the ECG signal was manually visualized to locate the 5-min segment with the lowest percentage of signal artifact.R-R interval detrending was completed using a smoothness priors regularisation with a cut-off frequency of 0.035 Hz.Artifacts were corrected using Kubios automatic artifact correction algorithm for beat correction 25 .Processing of HRV metrics from participant ECG signals was restricted to those presenting with < 10% artifact 26 .

Data collection
Clinical data was collected at admission and during daily follow-up using a clinical report form and electronic medical records.Demographics, past medical history, medications, and complications up to 30 days postoperative were recorded.Acquired ECG data was uploaded for offline analysis at time of discharge.
Postoperative complications including infection (pneumonia, urinary tract infection, surgical site infection), stroke, ileus, myocardial infarction, deep vein thrombosis (including pulmonary embolism) and death were identified at time of discharge and reviewed at 30 days postoperative using electronic medical records, discharge summary letters, and re-admission documentation.These specific complications were tracked due to their common presentation in this surgical population 6 .Complications were defined in accordance with nationally established standards (see Supplemental Information S2).

Analyses
Continuous participant characteristics were tested for normality using the Shapiro-Wilk test and presented using mean ± standard deviation.Binary characteristics were presented using frequency (percentage).Comparisons between groups were completed using independent samples t-tests, chi-square tests for associations or Fisher's exact tests as appropriate.Differences in pre-and postoperative HRV metrics between participants with and without complications were explored using Mann-Whitney U tests and presented as median[interquartile range(min-max)].The discriminatory power of pre-and postoperative HRV metrics to predict a 30-day postoperative complication was plotted using non-parametric receiver operator characteristic (ROC) curves with accompanying area under the curve (AUC) values and 95% confidence intervals (CI) generated using 2000 bootstrapped replicates.Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) with 95% CI were reported corresponding to cut-off values that maximized the sum of specificity and sensitivity (Youden Index).p < 0.05 was considered significant.Demographic and group comparison statistical analyses was completed using SPSS version 25.0 software (IBM, Armonk, NY, USA).ROC curve analysis and the calculation of sensitivity, specificity, PPV, and NPV values at the Youden Index for each metrics were completed using R Studio version 2023.03.0 with R statistical software version 4.3.0(R Foundation for Statistical Computing, Vienna, Austria) using pROC (version 1.18.5) and epiR (version 2.0.74)packages 27,28 .

Discussion
In this prospective cohort study on 30-day postoperative complications in radical cystectomy patients, perioperative HRV was analyzed in the pre-and postoperative periods.We found significant differences in postoperative HRV metrics between participants who did and did not develop postoperative complications.We found no difference in preoperative HRV metrics between those who did and did not develop complications.This contrasts with Ernst et al. 13 who reported significant preoperative differences in RMSSD and total power in those who developed complications after hip fracture surgery.However, our cohort included elective surgery patients as opposed to patients requiring urgent surgical repair 13 .Our finding of no preoperative differences may be explained by the timing of physiological stressors on the patient.Those presenting for urgent hip fracture surgery likely had a high degree of preoperative physiological stress.Our cohort of elective surgery patients likely presented with greater physiological stress after their highly invasive radical cystectomy procedure.
Postoperative SDNN, RMSSD, absolute LF and HF power and total power were significantly lower in patients who developed complications.Lower SDNN has been associated with increased morbidity and mortality in various clinical populations 4,[29][30][31] .Reduced RMSSD has been associated with complications in the post-surgical population and may be indicative of lower parasympathetic activity 13 .A study by Cha et al. reported that SDNN, RMSSD, LF and HF power and total power were significant in predicting adverse cardiovascular outcomes in Table 3. Discriminatory ability of heart rate variability (HRV) metrics acquired prior to anesthesia induction and 24-h post-ECG monitor placement for participants who did and did not develop complications within 30 days postoperative.ECG electrocardiogram, AUC area under the curve, CI confidence interval, SDNN standard deviation of NN intervals, RMSSD root mean squared standard deviation, VLF very low frequency, LF low frequency, HF high frequency, ms milliseconds, nu normalized units.diabetic patients 32 .HRV has been associated with frailty, age-related impairment of hemostatic mechanisms, resulting in critical loss of physiologic response to stressors 33 .This suggests that changes in HRV are associated with the ANS and represents alterations in general physiologic performance.
Our findings showed improved discriminatory power of postoperative HRV metrics including SDNN, RMSSD, absolute LF, absolute HF, and total power compared to preoperative values.The use of cut-off values with associated sensitivity and specificity may allow for simplified clinical utility of HRV metrics in practice through the use of real-time monitoring.As such, HRV could be a useful indicator to identify patients at risk of postsurgical complications through risk stratification via monitoring of HRV metrics and their relationship with established cut-off values.Though vital signs are essential in the monitoring and management of patients, the application of HRV as a measure of sympathetic and parasympathetic nerve system balance may demonstrate increased utility.Perioperative HRV monitoring assessed in real-time may enhance detection and early management of patients at risk of developing postoperative complications.Kasaoka et al. demonstrated such using real-time HRV metrics (LF, HF and LF/HF ratio) in the intensive care unit to immediately assess and investigate clinical conditions of critically ill patients 34 .
Our study is not without limitations.Complications may be underreported if they did not occur in hospital or community office without access to the Alberta province wide electronic medical data system.Our study used 5-min HRV segments for short term analysis 35 .Though an attractive approach for data analysis and clinical application, this approach is comparatively more sensitive to artifact than using longer ECG segments.We standardized our preoperative 5-min ECG segment between 10 min prior to up to the time of anesthesia induction when patients were supine.No such explicit standardization occurred for our 24-h post-ECG monitor placement measurements.As such, there is the possibility that patients could have been ambulatory given ERAS recommendations of early mobilization (up to 2-h postoperative day 0) 23 .
Our study was not powered to detect a specific difference between preoperative and postoperative HRV metrics, between participants who did and did not develop complications or on the discriminatory ability of HRV metrics.As such, we cannot conclude with certainty which HRV metric has the strongest discriminatory ability for risk stratification purposes.Our results suggest that postoperative monitoring of HRV provides improved risk stratification capabilities compared to a single short preoperative recording.However, our findings are not necessarily translatable to other surgical populations that have lower risks of postoperative complications or surgeries that may be associated with high preoperative physiological stress.We cannot discount the possible influence of both collected and unobserved confounding factors on reported differences in HRV metrics between groups and their discriminatory ability.Indeed, several participants were associated with comorbidities and medications that can affect the ANS and HRV.Except for BMI, no statistically significant differences were noted between groups.Multivariable logistic regression can be used to address confounding factors.However, following the often used 10 events per variable criteria for multivariable regression, where in this instance a complication represents an event, we are restricted to a maximum of two variables in any such model 36 .
Although these limitations must be considered when synthesizing our results, this work has developed a foundation for larger appropriately powered studies to determine if long-term monitoring is indeed superior to preoperative recordings across a variety of surgical and perioperative settings.Our analysis of HRV metric discriminatory abilities has allowed for the initial presentation of clinical cut-offs values to predict who will and will not develop postoperative complications.At this point, these should not be considered established reference values for risk stratification.However, further rigorous exploration of such values and implementation into clinical monitoring is strongly recommended to improve interpretation of HRV metrics (is patient above or below cutoff value?) while providing real-time visualization of current clinical status and potential future destabilization.

Conclusion
HRV was reduced in participants who developed complications 30-days postoperative.Postoperative HRV metrics showed improved discriminatory ability of participants who did and did not develop postoperative complications within 30 days.This suggests that longer-term perioperative HRV monitoring has the improved ability to stratify complication risk with respect to our specific cohort.Specific HRV metrics including postoperative SDNN, RMSSD, absolute LF and HF power, and total power demonstrated significant discriminatory power for development of complications.Our analysis allowed for the presentation of preliminary clinical cut-off values that presents an attractive tool for future risk stratification.The presented values should not be considered established reference values, but used a guide to further develop the concept of HRV as a real-time clinical tool for clinical status monitoring.The analysis and interpretation of HRV metrics can be difficult for the clinician to process.However, future appropriately powered studies is a worthy endeavour that would improve interpretation and permit real-time clinical assessment and risk stratification.

Figure 2 .
Figure 2. Non-parametric receiver operating characteristic (ROC) curves displaying the discriminatory power of postoperative root mean squared standard error (RMSSD) and postoperative absolute high frequency (HF) power in predicting 30-day postoperative complications.AUC, area under the curve.

Table 1 .
Participant characteristics of those developing and not developing a complication 30-days postoperative from radical cystectomy surgery.Data presented as mean ± standard deviation or frequency (percentage).n number, ACE angiotensin-converting enzyme.a p values in relation to comparisons between patients with no complications or complications at 30-days postoperative.

Table 4 .
Postoperative heart rate variability (HRV) metric cut-off values for the prediction of a complication within 30 days postoperative.Presented cut-off values correspond to the maximum value of the sum of the sensitivity and specificity (Youden Index) for each respective time and frequency domain HRV receiver operator characteristic (ROC) curve.CI confidence interval, PPV positive predictive value, NPV negative predictive value, SDNN standard deviation of NN intervals, RMSSD root mean squared standard deviation, VLF very low frequency, LF low frequency, HF high frequency, ms milliseconds, nu normalized units.